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      <li>在Raspberry Pi上部署预训练模型</li>
    
    
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  <div class="sphx-glr-download-link-note admonition note">
<p class="admonition-title">注解</p>
<p>点击 <a class="reference internal" href="#sphx-glr-download-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">这里</span></a> 下载完整的样例代码</p>
</div>
<div class="sphx-glr-example-title section" id="deploy-the-pretrained-model-on-raspberry-pi">
<span id="tutorial-deploy-model-on-rasp"></span><span id="sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"></span><h1>在Raspberry Pi上部署预训练模型<a class="headerlink" href="#deploy-the-pretrained-model-on-raspberry-pi" title="永久链接至标题">¶</a></h1>
<p><strong>作者</strong>: <a class="reference external" href="https://ziheng.org/">Ziheng Jiang</a>, <a class="reference external" href="https://makihiro.github.io/">Hiroyuki Makino</a></p>
<p>这是一个使用Relay编译ResNet模型并将其部署到Raspberry Pi上的示例。</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tvm</span>
<span class="kn">from</span> <span class="nn">tvm</span> <span class="k">import</span> <span class="n">te</span>
<span class="kn">import</span> <span class="nn">tvm.relay</span> <span class="k">as</span> <span class="nn">relay</span>
<span class="kn">from</span> <span class="nn">tvm</span> <span class="k">import</span> <span class="n">rpc</span>
<span class="kn">from</span> <span class="nn">tvm.contrib</span> <span class="k">import</span> <span class="n">utils</span><span class="p">,</span> <span class="n">graph_executor</span> <span class="k">as</span> <span class="n">runtime</span>
<span class="kn">from</span> <span class="nn">tvm.contrib.download</span> <span class="k">import</span> <span class="n">download_testdata</span>
</pre></div>
</div>
<div class="section" id="build-tvm-runtime-on-device">
<span id="id1"></span><h2>在设备上构建 TVM 运行时间<a class="headerlink" href="#build-tvm-runtime-on-device" title="永久链接至标题">¶</a></h2>
<p>第一步是在远程设备上构建TVM运行时间。</p>
<div class="admonition note">
<p class="admonition-title">注解</p>
<p>本节和下一节中的所有指令都应在目标设备上执行，例如Raspberry Pi。并且我们假设目标设备运行Linux。</p>
</div>
<p>由于我们在本地机器上进行编译，因此远程设备仅用于运行生成的代码。我们只需要在远程设备上构建 tvm 运行时间即可。</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git clone --recursive https://github.com/apache/tvm tvm
<span class="nb">cd</span> tvm
mkdir build
cp cmake/config.cmake build
<span class="nb">cd</span> build
cmake ..
make runtime -j4
</pre></div>
</div>
<p>成功构建运行时间后，我们需要在 <code class="code docutils literal notranslate"><span class="pre">~/.bashrc`文件中设置环境变量。我们可以使用:code:`vi</span> <span class="pre">~/.bashrc</span></code> 编辑:code:<cite>~/.bashrc`并添加以下行（假设您的TVM目录位于:code:`~/tvm</cite>）：</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span> <span class="nv">PYTHONPATH</span><span class="o">=</span><span class="nv">$PYTHONPATH</span>:~/tvm/python
</pre></div>
</div>
<p>要更新环境变量，请执行:code:<cite>source ~/.bashrc</cite>。</p>
</div>
<div class="section" id="set-up-rpc-server-on-device">
<h2>在设备上设置 RPC 服务器<a class="headerlink" href="#set-up-rpc-server-on-device" title="永久链接至标题">¶</a></h2>
<p>要启动RPC服务器，请在远程设备上运行以下命令（在我们的示例中是Raspberry Pi）。</p>
<blockquote>
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python -m tvm.exec.rpc_server --host <span class="m">0</span>.0.0.0 --port<span class="o">=</span><span class="m">9090</span>
</pre></div>
</div>
</div></blockquote>
<p>如果您看到下面的行，则表示RPC服务器在您的设备上成功启动。</p>
<blockquote>
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>INFO:root:RPCServer: <span class="nb">bind</span> to <span class="m">0</span>.0.0.0:9090
</pre></div>
</div>
</div></blockquote>
</div>
<div class="section" id="prepare-the-pre-trained-model">
<h2>准备预先训练的模型<a class="headerlink" href="#prepare-the-pre-trained-model" title="永久链接至标题">¶</a></h2>
<p>返回主机，主机应安装完整的TVM（带有LLVM）。</p>
<p>我们将使用`MXNet Gluon model zoo`中预先训练的模型&lt;<a class="reference external" href="https://mxnet.apache.org/api/python/gluon/model_zoo.html">https://mxnet.apache.org/api/python/gluon/model_zoo.html</a>&gt;`_. 您可以在教程ref:<a href="#id1"><span class="problematic" id="id2">`</span></a>tutorial-from-mxnet`中找到有关此部分的更多详细信息。</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">mxnet.gluon.model_zoo.vision</span> <span class="k">import</span> <span class="n">get_model</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="c1"># one line to get the model</span>
<span class="n">block</span> <span class="o">=</span> <span class="n">get_model</span><span class="p">(</span><span class="s2">&quot;resnet18_v1&quot;</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<p>为了测试我们的模型，在这里我们下载了猫的图像，并改变其格式。</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">img_url</span> <span class="o">=</span> <span class="s2">&quot;https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true&quot;</span>
<span class="n">img_name</span> <span class="o">=</span> <span class="s2">&quot;cat.png&quot;</span>
<span class="n">img_path</span> <span class="o">=</span> <span class="n">download_testdata</span><span class="p">(</span><span class="n">img_url</span><span class="p">,</span> <span class="n">img_name</span><span class="p">,</span> <span class="n">module</span><span class="o">=</span><span class="s2">&quot;data&quot;</span><span class="p">)</span>
<span class="n">image</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span>


<span class="k">def</span> <span class="nf">transform_image</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">image</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">123.0</span><span class="p">,</span> <span class="mf">117.0</span><span class="p">,</span> <span class="mf">104.0</span><span class="p">])</span>
    <span class="n">image</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">58.395</span><span class="p">,</span> <span class="mf">57.12</span><span class="p">,</span> <span class="mf">57.375</span><span class="p">])</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:]</span>
    <span class="k">return</span> <span class="n">image</span>


<span class="n">x</span> <span class="o">=</span> <span class="n">transform_image</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
</pre></div>
</div>
<p>synset用于将标签从ImageNet类的数量转换为人类可以理解的单词。</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">synset_url</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
    <span class="p">[</span>
        <span class="s2">&quot;https://gist.githubusercontent.com/zhreshold/&quot;</span><span class="p">,</span>
        <span class="s2">&quot;4d0b62f3d01426887599d4f7ede23ee5/raw/&quot;</span><span class="p">,</span>
        <span class="s2">&quot;596b27d23537e5a1b5751d2b0481ef172f58b539/&quot;</span><span class="p">,</span>
        <span class="s2">&quot;imagenet1000_clsid_to_human.txt&quot;</span><span class="p">,</span>
    <span class="p">]</span>
<span class="p">)</span>
<span class="n">synset_name</span> <span class="o">=</span> <span class="s2">&quot;imagenet1000_clsid_to_human.txt&quot;</span>
<span class="n">synset_path</span> <span class="o">=</span> <span class="n">download_testdata</span><span class="p">(</span><span class="n">synset_url</span><span class="p">,</span> <span class="n">synset_name</span><span class="p">,</span> <span class="n">module</span><span class="o">=</span><span class="s2">&quot;data&quot;</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">synset_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">synset</span> <span class="o">=</span> <span class="nb">eval</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">())</span>
</pre></div>
</div>
<p>现在我们想把Gluon model移植到一个可移植的计算图上。这就像几行代码一样简单。</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># We support MXNet static graph(symbol) and HybridBlock in mxnet.gluon</span>
<span class="n">shape_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;data&quot;</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">}</span>
<span class="n">mod</span><span class="p">,</span> <span class="n">params</span> <span class="o">=</span> <span class="n">relay</span><span class="o">.</span><span class="n">frontend</span><span class="o">.</span><span class="n">from_mxnet</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="n">shape_dict</span><span class="p">)</span>
<span class="c1"># we want a probability so add a softmax operator</span>
<span class="n">func</span> <span class="o">=</span> <span class="n">mod</span><span class="p">[</span><span class="s2">&quot;main&quot;</span><span class="p">]</span>
<span class="n">func</span> <span class="o">=</span> <span class="n">relay</span><span class="o">.</span><span class="n">Function</span><span class="p">(</span><span class="n">func</span><span class="o">.</span><span class="n">params</span><span class="p">,</span> <span class="n">relay</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">func</span><span class="o">.</span><span class="n">body</span><span class="p">),</span> <span class="kc">None</span><span class="p">,</span> <span class="n">func</span><span class="o">.</span><span class="n">type_params</span><span class="p">,</span> <span class="n">func</span><span class="o">.</span><span class="n">attrs</span><span class="p">)</span>
</pre></div>
</div>
<p>下面是一些基本的工作负载配置数据。</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">num_classes</span> <span class="o">=</span> <span class="mi">1000</span>
<span class="n">image_shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)</span>
<span class="n">data_shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">batch_size</span><span class="p">,)</span> <span class="o">+</span> <span class="n">image_shape</span>
</pre></div>
</div>
</div>
<div class="section" id="compile-the-graph">
<h2>编译计算图<a class="headerlink" href="#compile-the-graph" title="永久链接至标题">¶</a></h2>
<p>为了编译图形，我们使用图形配置和参数调用:py:func:<cite>relay.build`函数。但是，不能在具有ARM指令集的设备上部署x86程序。这意味着中继还需要知道目标设备的编译选项，除了参数:code:`net</cite> 和:code:<a href="#id1"><span class="problematic" id="id2">`</span></a>params`来指定深度学习工作负载。实际上，选择很重要，不同的选择会导致结果大不相同。</p>
<p>如果我们在x86服务器上运行这个示例进行演示，我们只需将其设置为 <code class="code docutils literal notranslate"><span class="pre">llvm</span></code>。如果在Raspberry Pi上运行它，我们需要指定它的指令集。如果要使用真实设备运行本教程，请将:code:<a href="#id1"><span class="problematic" id="id2">`</span></a>local_demo`设置为False。</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">local_demo</span> <span class="o">=</span> <span class="kc">True</span>

<span class="k">if</span> <span class="n">local_demo</span><span class="p">:</span>
    <span class="n">target</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">Target</span><span class="p">(</span><span class="s2">&quot;llvm&quot;</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
    <span class="n">target</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">arm_cpu</span><span class="p">(</span><span class="s2">&quot;rasp3b&quot;</span><span class="p">)</span>
    <span class="c1"># The above line is a simple form of</span>
    <span class="c1"># target = tvm.target.Target(&#39;llvm -device=arm_cpu -model=bcm2837 -mtriple=armv7l-linux-gnueabihf -mattr=+neon&#39;)</span>

<span class="k">with</span> <span class="n">tvm</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">PassContext</span><span class="p">(</span><span class="n">opt_level</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
    <span class="n">lib</span> <span class="o">=</span> <span class="n">relay</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span>

<span class="c1"># After `relay.build`, you will get three return values: graph,</span>
<span class="c1"># library and the new parameter, since we do some optimization that will</span>
<span class="c1"># change the parameters but keep the result of model as the same.</span>

<span class="c1"># Save the library at local temporary directory.</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">tempdir</span><span class="p">()</span>
<span class="n">lib_fname</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">relpath</span><span class="p">(</span><span class="s2">&quot;net.tar&quot;</span><span class="p">)</span>
<span class="n">lib</span><span class="o">.</span><span class="n">export_library</span><span class="p">(</span><span class="n">lib_fname</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">输出:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tvm_chinese/tvm/python/tvm/relay/build_module.py:333: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
  DeprecationWarning,
</pre></div>
</div>
</div>
<div class="section" id="deploy-the-model-remotely-by-rpc">
<h2>通过RPC远程部署模型<a class="headerlink" href="#deploy-the-model-remotely-by-rpc" title="永久链接至标题">¶</a></h2>
<p>使用RPC，您可以将模型从主机远程部署到远程设备。</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># obtain an RPC session from remote device.</span>
<span class="k">if</span> <span class="n">local_demo</span><span class="p">:</span>
    <span class="n">remote</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">LocalSession</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
    <span class="c1"># The following is my environment, change this to the IP address of your target device</span>
    <span class="n">host</span> <span class="o">=</span> <span class="s2">&quot;10.77.1.162&quot;</span>
    <span class="n">port</span> <span class="o">=</span> <span class="mi">9090</span>
    <span class="n">remote</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="n">host</span><span class="p">,</span> <span class="n">port</span><span class="p">)</span>

<span class="c1"># upload the library to remote device and load it</span>
<span class="n">remote</span><span class="o">.</span><span class="n">upload</span><span class="p">(</span><span class="n">lib_fname</span><span class="p">)</span>
<span class="n">rlib</span> <span class="o">=</span> <span class="n">remote</span><span class="o">.</span><span class="n">load_module</span><span class="p">(</span><span class="s2">&quot;net.tar&quot;</span><span class="p">)</span>

<span class="c1"># create the remote runtime module</span>
<span class="n">dev</span> <span class="o">=</span> <span class="n">remote</span><span class="o">.</span><span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">module</span> <span class="o">=</span> <span class="n">runtime</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">(</span><span class="n">rlib</span><span class="p">[</span><span class="s2">&quot;default&quot;</span><span class="p">](</span><span class="n">dev</span><span class="p">))</span>
<span class="c1"># set input data</span>
<span class="n">module</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="s2">&quot;data&quot;</span><span class="p">,</span> <span class="n">tvm</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;float32&quot;</span><span class="p">)))</span>
<span class="c1"># run</span>
<span class="n">module</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
<span class="c1"># get output</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># get top1 result</span>
<span class="n">top1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;TVM prediction top-1: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">synset</span><span class="p">[</span><span class="n">top1</span><span class="p">]))</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">输出:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>TVM prediction top-1: tiger cat
</pre></div>
</div>
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